{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activation Functions\n",
    "\n",
    "This function introduces activation functions in TensorFlow\n",
    "\n",
    "We start by loading the necessary libraries for this script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.framework import ops\n",
    "ops.reset_default_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start a graph session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize the X range values for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_vals = np.linspace(start=-10., stop=10., num=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activation Functions:\n",
    "\n",
    "ReLU activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.   3.  10.]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(tf.nn.relu([-3., 3., 10.])))\n",
    "y_relu = sess.run(tf.nn.relu(x_vals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ReLU-6 activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  3.  6.]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(tf.nn.relu6([-3., 3., 10.])))\n",
    "y_relu6 = sess.run(tf.nn.relu6(x_vals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sigmoid activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.26894143  0.5         0.7310586 ]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(tf.nn.sigmoid([-1., 0., 1.])))\n",
    "y_sigmoid = sess.run(tf.nn.sigmoid(x_vals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hyper Tangent activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.76159418  0.          0.76159418]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(tf.nn.tanh([-1., 0., 1.])))\n",
    "y_tanh = sess.run(tf.nn.tanh(x_vals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Softsign activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.5  0.   0.5]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(tf.nn.softsign([-1., 0., 1.])))\n",
    "y_softsign = sess.run(tf.nn.softsign(x_vals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Softplus activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.31326166  0.69314718  1.31326163]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(tf.nn.softplus([-1., 0., 1.])))\n",
    "y_softplus = sess.run(tf.nn.softplus(x_vals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exponential linear activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.63212055  0.          1.        ]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(tf.nn.elu([-1., 0., 1.])))\n",
    "y_elu = sess.run(tf.nn.elu(x_vals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the different functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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Qk5SQkBBzq/0Z3bx584zD4Ujy/OLi4oy/v7/x9/c3cXFxxhhjvv/+e3P//feb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\nAGECOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f130831a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oSgkXF5dcvQbr16+3OH5CCCGEyC/poSglevTowbPPPkvv3r3p2rUr//77L59++ikNGza0\neMtDCCGEyA9JKEoYa2trPP3000RHRzN79mxWrFhBo0aN+O6775gzZw579uy5qXNYWrvDVlshhBCl\nhyrug+eUUmHAjh07dhAWFmaxzY313m21ESWTvPdCCGHdjd+RQLjWukD3wGUMhRBCCCEKTBIKIYQQ\nQhSYJBRCCCGEKDBJKIQQQghRYJJQCCGEEKLAJKEQQgghRIFJQiGEEEKIApOEQgghhBAFJgmFEEII\nIQpMEgohhBBCFJgkFEIIIYQoMEkohBBCCFFgklCIXKZOnUqdOnVwdXW1y4Ja/fv3p1atWnaITAgh\nRFElCUUJsHfvXnr37k3NmjXx8vKiWrVqdOnShQ8++CDf51q1ahWvvvoq7dq1Y+7cuUyaNAmAyZMn\ns3Tp0luKTymFyST/1IQQoiRzdXYAomA2btxIp06dCA4OZsiQIQQFBXHy5Ek2b97Me++9x3PPPZev\n861duxYXFxfmzJmDi4tLZvmkSZPo06cP999/f75jnD17Nunp6fk+TgghRPHh0IRCKdUOeBkIByoD\nvbTWy/I4pgMwHWgEnAAmaq3nOTLO4mzixImUKVOG7du34+fnZ1YXHR2d7/OdP38eLy8vs2SioFxc\nXOx6PiGEEEWPo/uhfYBdwDBA59VYKVUT+Bn4DWgGvAvMVkp1dlyIxduRI0do1KhRrmQCoHz58pk/\np6WlMWHCBOrWrYunpye1atVi7NixJCcnZ7YxmUzMmzeP69evYzKZcHFxYd68eZhMJuLj45k7dy4m\nkwmTycRTTz0FQFxcHMOHD6dWrVp4enpSqVIlunTpwq5duzLPa2kMxeXLl3n88ccJCAggMDCQAQMG\nsGfPHkwmE/Pnzzc71s/PjzNnztCrVy/8/PyoWLEiL7/8Mlrn+U9KCCFEIXFoD4XW+lfgVwCllLqJ\nQ54BjmitX8nYP6CUaguMAFY7JsriLTg4mM2bN/P333/TqFEjq+0GDhzI/Pnzefjhh3nppZfYsmUL\nkyZNYt++fSxevBiABQsW8Mknn7Bt2zbmzJmD1po2bdqwYMECBg4cyO23386QIUMAqFOnDgBPP/00\nP/zwA88//zyhoaFcunSJDRs2sG/fPpo3bw4YYyiyv/1aa3r06MH27dt59tlnadCgAUuXLuXJJ58k\n5z8TpRTp6el07dqVO+64g+nTp7NmzRpmzJhB3bp1efrpp+369ymEEOIWaa0LZQPSgZ55tPkDmJGj\nrD8QY+OYMEDv2LFDW7Njxw6dV5vspm+crqtOr5q5DVk2xGrbr/Z8patOr2q1PjYx1uxcVadX1Wdi\nz9xUHDdj9erV2s3NTbu6uuo2bdroV199Va9atUqnpKRkttm9e7dWSumnn37a7NiXX35Zm0wmvW7d\nusyy/v37az8/v1zX8fX11QMGDMhVXqZMGf3888/bjLF///66Vq1amfuLFy/WSin9/vvvm7WLiIjQ\nJpNJz5s3z+xYk8mkJ06caNY2LCxM33bbbTavq3X+33shhChNbvyOBMJ0AT/ni9qgzCDgfI6y84C/\nUspDa51UGEHEJsVy+trpzP3LiZetto1PiTdrm5NG56pP02kFDzLD3XffzcaNG5kyZQorV65k8+bN\nvP3221SoUIE5c+bQo0cPli9fjlKKESNGmB07atQopk2bxvLly2nfvv0tXb9MmTJs3bqVs2fPUrly\n5Zs6ZuXKlbi7uzNo0CCz8mHDhvH7779bPCZnT0S7du1YsGDBLcUsRFGQkADx8VCunHn5tm1QtSpU\nqZJVdvw47NwJvXqZt/3pJ6hcGVq2zCo7dQpWrIDISPD1zSr/+WdwdYV77skqi4mBBQvgoYfMr/fb\nb3DxIvTtm1WWnAyzZkG3btCgQVb5ls2a3TvTGNI/xSjw8gKMtq1amcf299+wat4ZXnzkPCadBunp\nkJbGl7+Uo1q5BDo2j4HQUKhUiePHYfFiGDQI/P2zzvHDnBhc/91Pz5ZnQGtIT+fyNTfmrqvJI3cc\np2pgPDzyCLi4sHq18ToefTTr+JQUeH/4YbpX3klI0BWjUGs2H6nI7pNlefqufRAcDF27AvDhh8br\nuO22rHP89ResemMjw9vvJPMBNq2Zv6UB1QPj6Fj/NNx9N4SEWH8dH53DNWobPRsfySy7fN2DuVsb\n8kiLg1Qtcx2GDQNXV+uvY9h+upfbQkilmMzyzceC2H2mAk+32Qu1a0PPnjZfx4JRUdhNQTOSm924\nuR6KA8CrOcq6A2mAu5VjSnUPRXYpKSl6+/btesyYMdrb21t7eHjoffv26aFDh2pXV1edmpqa65jA\nwED98MMPZ+7nt4di0aJF2tvbW7u4uOhWrVrpN954Qx85csSsTc4eiq5du+qaNWvmOteePXu0UipX\nD4W3t3eutm+88YY2mUxW/iaySA+FcJT0dGPLbvFirT/7zLzs0iWt69bVetUq8/KxY7UODs59Xl9f\nradPNy+bPVtryH29hg21fvFF87KVK422x46Zl9/TIV4/0PK41h9/bFxgwgS9b8gMDVr/ed8Urfv1\ny7zA4MFa5+wAvHbNOO9Cv0FaBwRo7eWltaurHs9YXZnTRmWvXpnty5TR+u23zc/x+edGs1RMxg8Z\nWxN26xd4x9hfsEBrrfXq1ZZfR7ewc/oBFpsdv48GGrT+H3caZYmJVl9HXFzG6+ARs3OYvY777iv4\n65g/3/brCC8qr6Pk9lCcAyrlKKsIxGqtky20zzRixAgCAgLMyiIjI4mMjMx3ECNbj2Rk65E31fbR\nJo/yaJNHrdb7efhxauSpfMdwK1xdXQkPDyc8PJx69erx1FNP8d13391IvHKNT7CHPn36cNddd7Fk\nyRJWrVrFtGnTeOutt1iyZAldMzL8gpInRIQzpafDtWuQ/dfL5s3GF9A//4SMoUIALF9ufAvP3vnm\n4QH//mt8w8zO0xMSE3Nfz83N+PaZnYtKB0ykL1mKy+WLcOECXLyIy5kR6B93wubJMGECdO7Mjf/m\nOseYZXX1KuzcDtuHZittAIyAn5YBG2H2bCNgW65dA65arktNtX3szcjrEfOb+TWW88ULAHbuXEjP\nngsz90+eBKvv5S0oagnFJqBbjrIuGeU2zZw50y6zOpYULVu2RGvN2bNnqVmzJunp6Rw6dIgG2foq\nL1y4wJUrVwgODs7zfLaSkUqVKjF06FCGDh1KdHQ0LVq0YOLEiVYTiuDgYNatW0diYiKenp6Z5YcO\nHcrHKxSicHTsCLVqwdy5WWXVqsH163DmjHlCUbYsXM5xh9Tb2yjP+TkZEgIRERqiLxkf0hlPQj37\nLISHm7cND4fJajQ89BZGZ69hNGepeuU0HN8CJ04A0LChkRfkvJUy6pFTuOx8z6ysCmf4ikepz0Gj\nIDERPDwYNAiuXDE/3tMTvrnvK1rvPgXeIUbm4+7OgymHaZr6AVTqCE2aZLb//HPj7kV2HTrAt10/\nR9V8GlxN4OICJhNTT26moo+Gyq9CxuDyJk3g228h28NqALw2KgWX/8VDrbfBZAKlqJLkwaIDywmp\n9zj4PWzc2wGGDMn9Ojw8YNH//cXtXj2hXAQoBUrx0JkyNL/4NzSfAzVqZLb/4gvLr2PRC//D1HKe\nWYIz7a+LVPCrBzUXQOvWma9j0SILr2O0CZe9flD/a25kgVXiXVm0ZwMhDUeC/3N5v47/HuT2Mo9B\n+Qcyyx865U/z84ch/Buz+1jG64ikQYOsL9lHj8Ki1+fyn6/WYxcF7eKwtWE8NtoMaI7xv2B4xn71\njPrJwLxs7WsCccBbGKnzs0AycLeNa9j9lkdxsnbtWovlb731llZK6XfffTdzUObQoUPN2rzyyis3\nPSgzKChIP/DAA2ZlaWlp+urVq7natmrVSrdq1crsnJYGZb733nuZZenp6bpTp04WB2VaikdueQh7\nO3xY68hIraOjzcufe864ZZFderrWTZtqvXy5efn+/Vpv2GDh5GlpWv/7r9ZLlmg9YYJxe+H227UO\nDDS6pjt3zjvAqlXNurZzbe+8Y/v4Eye0fvddrefM0frbb7X++Wetf/tN602btN69W+uDB7W2cFtU\nlGzFaVBmS2BtRrAaY8IqgHnAUxiDMKvfaKy1PqaUuheYAbwAnAIGaq3XODjOYuv5558nPj6eBx54\ngJCQEJKTk9mwYQOLFi2idu3a9O/fH39/f5588kk+/fRTYmJiaN++PVu2bGH+/Pk8+OCDNzUgMzw8\nnDVr1jBz5kyqVKlCrVq1aNCgAdWqVaN37940a9YMX19fVq9ezfbt25kxY4bVc/Xq1YtWrVoxatQo\nDh06REhICMuWLeNKRgruiFszQuTF29v4RhwRAQMHZpV36WL0RCQng7u7UaYU7N6d+xzZByuaef55\nY5SiNceP5x3giy9CUhJUrGhsFSoYW7lyUKaM8W3flurV4YUX8r6OELeqoBmJszdKeQ/FypUr9aBB\ng3TDhg21v7+/9vT01PXr19fDhw/XFy5cyGyXlpamJ0yYoOvUqaM9PDx0cHCwHjt2rE5OTjY7X//+\n/bW/v3+u6xw4cEB36NBB+/j4aJPJpAcMGKBTUlL0q6++qlu0aKEDAgK0n5+fbtGihf7kk09ynbN2\n7dpmZZcuXdL9+vXTAQEBOjAwUA8cOFBv3LhRK6X0okWL8oznjTfe0C4uLnn+/ZTk917cuq1btX7t\ntdzlHTtqnaMjLm/Xrmmd4/9RLu+/b7lXoUYNrSMitB4+PJ8XFcI+7NlDoXQxH7yilAoDduzYscPq\nGIqoqCjCw8Ox1UY4348//shDDz3En3/+SeuM+48FJe+9sOS77+Dhh2HTJrjjjqzy06eNL/9ubjYO\nTk01DlyxAtatM57zXLXKGGxhzbZtMHGicUO9SRPjpnzdupmPWAphD+k6naTUJBJTEzEpEwGeARbb\nHbtyjKizUTwY+mDm70ggXGtdoGdIi9qgTFFKJCUl4ZFtNHl6ejrvv/8+/v7+8sEvHO6hh4zP9Nmz\nzROKqlWtHHDpkpFALF8OK1caEzhkt26d7YTittvgxx8LGrYoRrTWJKUl4e7ijklZXuXih30/cC3p\nGgmpCSSkJPBI40eo4lfFYtvBywYTVjmMZ257xmL9m+ve5I0/3sjc79OwD4v6LLLYds2RNQz+aTBp\n/2e/OZFAEgrhJDfGfrRu3ZqkpCQWL17M5s2bmTx5slmiIURB7dgBY8YYkwv5+BhlJhP88ovxtEae\ntIZmzYzuC0tCQowxDKLYO3blGGnpadQpW8di/dbTW/ly95dcT7nO9ZTr1PCvwdQuUy22XX1kNV0X\ndOX48OPUCKhhsc3AZQO5kpj1+EZ4lXCrCcW2M9vwcLX+u9HNxbxbLTHVwjPJGTxcjPMkp9mcjSHf\nJKEQTtGxY0dmzJjB8uXLSUxMpG7dunzwwQc884zl7FuIW+XnB//7H4wcCZ98klVes+ZNnkApY4rK\nDz809suUMWZRvPde6NwZgoLsHbK4SSlpKRyOOUw1/2r4uvtabDNu7Tj2X9pPbFIs15KuMfausdxT\n9x6LbZ/75TlcTC4s7bvUYv2hS4f4YNsHmftNKzVlKpYTCm83b8CYTdkaL1cvrpCVUCSkJFht6+nq\naTNJqBFQg9ur3o6Xmxeerp6EVw632rZllZZM7TzVas/JrZKEQjjFrU46JkR+1a8PM2fC/PnGNAvZ\npj65ef37Gwfedx+0aZPHIAtREKdjT7NgzwJiEmO4nHCZtPQ05tw/x2LbU7GnCP0wlNWPr+bu2ndb\nbLPqyCo2n9qcuX/i6gmr1/bz8OPC9QtW633cfcz2rydft9q2vHd52ge3x81k/d/K+I7jSU5LxtvN\nGy9XL5pWamq17Zon1uDu4m61vl/TfvRr2s9qfXahFUIJrRCad8N8koRCCFGiXLliPOJZsWJW2eDB\nxqOgZk9Wam2Mh3j3XXjlFdtjIFq2NF+UQuSSmJrIxesXuXD9AmW9ylIrsJbFdpP/N5lVR1ax9sm1\nFuvPxp3lP7/9J3PfzeTG7J6zLT5OfmPQ4dVE67M9+nv4m+3bSgK61unKpfhLVuvvCr6LrYO24uPu\ng4+bj9VeEYCQ8iGs67/Oaj3AoLBBNuuzs3WtokISCiFEifLkk8ZU12vXZiUVSmVLJlJSjAkn3n4b\n9u41yvyU6DAFAAAgAElEQVT9bScUpdy209sI8g2iekB1i/WDlg1izs6sXoRRrUcxrcs0i21T01PZ\nd3Gf1WsFegaa7aekp5CQmpB5CyG7AI8Anmj2hNVxBwCf9vgUjcbP3Q9/D/9cYw2y69+8v9U6gLJe\nZSlbtazNNqWZJBRCiBLl7beN3KBvXzBbvDYhAT79FKZPv7GIQZZdu4xEoxTdyohLjuPk1ZOcij3F\nydiTPNzoYavfgrsu6MrLbV7mtXavWazP2QsQHR9t9bpV/Krg4+5jzFtgodehil8VFj+8mEDPQAK9\nAinjWQZPV8v3qVxMLszrNc/qtQCCy+S9tICwD0kohBAlSoMGRu+E2TpVq1cb4yDOnDFv3KoVvPoq\n3H9/3jNNFjNXEq/g7+FvceBdanoqAVMCSNdZ64KEVQ6jeVDzXG0BKvtV5mzcWavXCi0fStsabano\nU5GK3hW5s8adVtsODBvIwLCBVuu93Lx4MPRBq/Wi6CpVCcW+fda72UTJJO95yZeaasxI7ZNtvFyu\nKbCrVYNz57L2773XGDfRrl3mwkwlyYpDK+j+dXdOjThFVf/ck2u4mlwJ8g3izLWsBOvk1ZNWE4oh\nYUOo5m/9GdvB4YMZHD644IGLYq1UJBTly5fH29ubfv1ubgSsKFm8vb0pn3OpP1FiTJkCX34JCxeC\n1TnRQkPhiSeMCanGj4em1kfTF1WHLx9m0d+L2H9pP/uj9+Pl6mV10N+NeQ+OxByxmFAA3N/gfmKT\nYqnuX53qAdVpXLGx1Wu/eMeLBY5flHylIqGoUaMG+/btIzra+n09UXKVL1+eGjUsTywjir8+fWDJ\nEmjbFo4dM3+6w8xnn2UuB11UxSXHWR3HcOzKMUb/Pjpz38vVi3SdbvGWRq3AWnSo2QEXk/XbOLPu\ntbFYmRC3oGj/77KjGjVqyIeKECVQgwawcW0S69dDxYo2Zlkt4snE2N/H8s1f3/DvC/9arM/Zg1DR\npyKX4i9RwadCrrbebt5WH8sUwlGK9v8wIYTIy6ZNeAwcSOcHHoAeE50djUUJKQlsP7MdgHbB7Sy2\nqVe2HodjDnMt6Rp+Hn656iv6VOS7Pt9Rr2w96pWrZ/ExSiGcSRIKIUSxs2gRVC+fQOufRhsTU2kN\nhw/DgAHGKp5FxI/7f2Tyn5OJOhtFanoqnWp14rcnfrPYtmWVlnSq1YlLCZcsJhRKKXo37O3okIW4\nZZJQCCGKFa3hs7cuszbKnzfxZjQaBcZAS7NnRZ1Pa83W01sz97ec2kJqeiqupty/ehtVbGQ12RCi\nOLDvyiBCCOFICQmoF1/gl6hKvMZkTlMV5ekJU6fCpk3Gyp+F6JdDvzD297FW69vXbI9JmQgtH8rA\nFgN5r9t7pKYXraRHCHuRHgohRPHRty8sW4YbMIH/Q7e7C+bsgXr1nBLOkZgjvL3hbV6981WLtynK\nepXl8iuXM9ecEKIkkx4KIUTx8Z//GDNaenrC+++j1q11eDIRkxBjta5b3W6kpKew9pj1JyokmRCl\nhfRQCCGKhR9/hEOHWvP8B7PxbHcbNGrksGtdvH6RhX8t5Ms9X3Lh+gWOvnjU4nwPdcrWYffQ3TYn\nhRKitJCEQghRLPz9N4wbB+9X6c+2bVDJQdf55+I/NPu4mdlYh/XH19OhZgeL7ZtWKn6zbgrhCHLL\nQwhRLIwZA//8A4MH25gN0w5Cy4cSWj40cz+schgpaSmOu6AQJYT0UAghio6YGIiKgogIi9X168Pr\nrzs2BKUUo9uNZte5XTze9HEaVXTcrRUhShJJKIQQRcOePfDAA3D2LGzdCo0dMy5Ba81Lq16ibtm6\nPHPbMxbb9G3cl76N+zrk+kKUVHLLQwjhfIsWQevWcOQIJCTA0KGgNefPwz33GFNM2ItSiksJl5iw\nfgKJqYn2O7EQpZwkFEII50lLg1dfhUcegfh4oyw8HL7+GpTi+HE4ehTatIHZs+132dHtRhOXHEfU\n2Sj7nVSIUk5ueQghnOPyZYiMhFWrssqeeAI+/hi8vABo1Qr27YPVq+17B6R+ufqcHXUWH3cf+51U\niFJOeiiEEIUvNhZuvz0rmXBxgffeg7lzM5OJG0wm6NoVqla9+dMfu3KMngt7cjr2tNU2kkwIYV+F\nklAopYYppY4qpRKUUpuVUrfZaPukUipdKZWW8We6Uiq+MOIUQhQSf3/o2dP4uUIF+O03eP55UKpA\np9VaMydqDk0/aspPB3/itd9es0OwQoib4fCEQin1CDAdGAe0AHYDK5VS5W0cdhUIyrYFOzpOIUQh\ne/ttGD4ctm+H9u3NqvbsgcmTITo6f6ec9L9JDPppENeSrwHGhFRxyXH2ilgIYUNh9FCMAD7RWs/X\nWu8HhgLxwFM2jtFa64ta6wsZ28VCiFMIUZhcXGDmTKhRI1dVVBSMHw/VqsFa68tk5PJUi6co61XW\n+Ln5U+x5Zg++7r72ilgIYYNDB2UqpdyAcGDSjTKttVZKrQFa2zjUVyl1DCPhiQJGa63/cWSsQoii\no39/uO8++PxzY2DmzarsV5k5Pefgoly4r8F9DotPCJGbo5/yKA+4AOdzlJ8HGlg55gBG78UeIAB4\nGdiolGqktbY+wkoIUfRofcvjIsqVg5dfzv9xvUJ63dL1hBAF46ynPBSgLVVorTdrrRdorfdorf8H\nPAhcBIYUZoBCiAJautToZkhOtvupE1IS7H5OIUTBOLqHIhpII/fCgBXJ3WthkdY6VSm1E6hrq92I\nESMICAgwK4uMjCQyMvLmoxVCFJzW8NZbMHq08fOwYfDppzfVU7FhA1y9asyOabLydWd/9H46f9mZ\nT3t8Srd63ewcvBAl18KFC1m4cKFZ2dWrV+12focmFFrrFKXUDiACWAaglFIZ++/dzDmUUiagMfCL\nrXYzZ84kLCysYAELIQomIcFYDvSrr7LK4uIgNRXc3PI8/Kuv4KOPoG5d40lSC+M1qRFQg9DyofRd\n3JfNAzcTWiE0dyMhRC6WvmRHRUURHh5ul/MXxi2PGcAQpdQTSqkQ4GPAG5gLoJSar5TKHLSplHpd\nKdVZKVVLKdUC+ArjsVE7TrwrhLC7M2eMxz+zJxMTJhjTaN9EMgHw4YewcSPce6/xhIcl3m7eLItc\nxtDwoZT3tvX0uRCiMDl86m2t9aKMOSfGY9z62AV0zfYoaDUgNdshgcCnGPNPxAA7gNYZj5wKIYqi\nbdugVy8jqQDw8YEvvzRWD80HpYw1wlrbegYM8HT15K3Ob91isEIIRyiUtTy01rOAWVbqOuXYHwmM\nLIy4hBB2kJ4OQ4ZkJRPBwcaAzGbNnBuXEKJQyVoeQoiCMZng22+hTBlo2xa2bs13MnHhgrHlpLXF\nh8GEEEWQJBRCiIKrXx/++MMYSVmxYr4Pf+cdqFIFunWD/Rk3N1PTU+mxsAdzd821b6xCCIeQ5cuF\nEPbRtOktHzpypPFEx8KFEBholL3+++v8cugXfjn0C4cuHWJixEQ7BSqEcATpoRBCOF358jB0qNHJ\nUakSLDuwjCkbpgDganKle73uTo5QCJEXSSiEEHmLj4effy60y20+tTnz57fvfps7a9xZaNcWQtwa\nueUhhLDtwAHo0wf++gtWr4aICLudOj3d+DPnrJiTIibRpGITVh5eyfA7htvtekIIx5EeCiGEdd9+\nCy1bwt69xjTagwcbs17aycqVULs2vPYaHD5sXhfZJJK5veaibnFxMSFE4ZKEQgiR27VrMGAA9O1r\nTJ0N0LChcdvD1X4dm8HBxpMdn35qdIAIIYovSSiEEOY2b4bmzWHu3Kyyfv2M+SUaNrTrpRo2NNbu\nOHvWmG5bCFF8yRgKIUSWmBjo3DmrV8LPz1hgo1+/m1ot9Fa5uzvs1EKIQiI9FEKILIGBMHmy8XPr\n1rBrFzz+uEOTicsJlxm2fBjn4s457BpCCMeThEIIYW7YMJg/H9avN0ZMOkB8PDz2mDGR1Zajf/PN\n39/Q4IMGrDi0wiHXE0I4niQUQghzShm9EnYcfJnT6dPG06iPPgo/vtuOQ88fYkDzATStdOuzbQoh\nnEvGUAhR2sTFga+vU0OoVw+2b4cTJyAtDcp6leWde95xakxCiIKRHgohSou0NPjsM6hVy5gAogio\nUcMIRwhR/ElCIURpsGkT3H47DBkC0dHw4ouQnOzsqIQQJYgkFEKUZHv3Qq9e0KYN7NiRVd68OVy/\n7pSQfvvNGD8hhChZJKEQoiQ6d86YO6JZM1i6NKu8SRNYtw6++SZrnfBC9sILEHL7CZo1s+ss3kII\nJ5OEQoiSyMPDmCZba2O/alX45BOIioL27Z0a2uLfjmEaUYvrfTrx+/FVTo1FCGE/klAIURIFBsIr\nr0C5cjBtGhw6ZIyfcOCjoDdrzp4PSSedw2lr2XZ6m7PDEULYifN/uwghHGP4cHjuOfD3d3Ykma4n\nX2f2ztkAeLh4MCR8iJMjEkLYi/RQCFHcHDgAL70Eq/K4XeDtXaSSifh42HN2H+4uxsIdkU0iqeBT\nwclRCSHsRXoohCgOLl6ExYvh66/hf/8zyvbtgy5dnBtXPnz8MYwf35J7epygxWOL6BEe5uyQhBB2\nJAmFEEXVlSvGExrffAOrVxsTU2W3dq2xOqiTntbIr+7dITYWfvzRgzdqPU5IRWdHJISwJ7nlIURR\ntWQJ9O8Pv/5qnkyEhMD06ca81cUkmQAj7DfeMBYwDQlxdjRCCHuTHgohiqpu3bJ+rlED+vY1tubN\nHbqcuBBC3ApJKIQoLMnJsGcPbNsGW7YYK2SNGWO9fVCQ8chnmzZwxx2SRAghijRJKIRwlP374fff\njemvd+40tuzrZzRpYjuhABg1yrExFoLERGP2786d4f77oW5dZ0ckhHCEQhlDoZQappQ6qpRKUEpt\nVkrdlkf7PkqpfRntdyulutlqL0SRtHQpDBtmPN6wZUvuxbgOHTJGKZZwW/49wI6GnfnP95/wf1Mu\nOjscIYSDOLyHQin1CDAdGAJsBUYAK5VS9bXW0Rbatwa+Bl4FlgOPAj8qpVporf9xdLxCZDp4EM6c\nMd9OnoTjx43ts8+gZ0/rxzdpYr5fvz60apW1NWsGnp6OfQ1FgF+56zRrrllb5lnq33EdGOnskIQQ\nDlAYtzxGAJ9orecDKKWGAvcCTwFvW2j/IrBCaz0jY3+cUqoL8BzwbCHEK0qClBS4ds3Y4uLg6lWj\nN+DGnwEB8PDDts/RqpXR3ppjx/I+ftYsI7Fo0sS4ZikUVjmMNU+s4eL1i7i5uDk7HCGEgzg0oVBK\nuQHhwKQbZVprrZRaA7S2clhrjB6N7FYC9zskyJJEa0hPz1oQSuus7ca+uzuYbNzpio83tvT0rHNl\n/9nFxVhoypa9e40P8bS0rC011djS0oxv6g0aWD/+wgVjIaukJGNLTs76OSnJuCn/4YdQubL1c4wZ\nA1OnWq9v2TLvhKJCBcsJhVJQpYrtYwHKl4dnnsm7XSkhs2IKUbI5uoeiPOACnM9Rfh6w9okSZKV9\nkM0rtW1rfNhZ8sor8Prr1o89eBDC8pi1b/NmaNzYev3kyTBhQtb+jQ/xG+rVM0b42xIWZnwY5zzH\njT/HjIHx460ff+AAhIbavsaePbm74rObNg3GjbNeHxJizNBoS9++8I+Nu1PjxhkTElhz8SL83//Z\nvsaECbYTCl9f28dfvmy7Hozlv+PijOQh+1a9upGYiTytXg1ubsZ/zyKwLpkQwoGc9V9cATrPVvlo\nPyIhgZwdypEZW67BcDlpDdev226Tnm67PjUVEhKs1ycl2T7+xjlSU63X50xSbkVe58jr0cS8/h7A\nemJ3g63XCDf3YZ2YaLs+JMSYmtHXF3x8jNsNAQHG2hYBAUbvQ15sJVbipsycCStWGPNvHT5crObh\nEqLEWbhwIQsXLjQru2rrtm4+OTqhiAbSgEo5yiuSuxfihnP5bA/AzNq1CfPyslxZMY85ft3dbfc+\nQN6D5ypVgqZNzcuyfzgHB9s+HozeBTc38+Nu/KxU3rcavL3hzjtzH3djA+PD1ZZ69eDee43bIiaT\ncdyNP11cbPcK3NCvH5w9a7R3cTG+mt7409XVmFfBlqpV4eefwcPDeG88PLJ+9vIy3ovy5W2f4+GH\n876lIRzu558hKsro4JNkQgjnioyMJDIy0qwsKiqK8PBwu5xfaXt867V1AaU2A1u01i9m7CvgBPCe\n1jrXTW6l1DeAl9b6/mxlG4DdWutcgzKVUmHAjh07dhCW120LIYQQQmTKllCEa62jCnKuwrjlMQOY\np5TaQdZjo97AXACl1HzglNZ6dEb7d4E/lFIjMR4bjcQY2Dm4EGIVQtjJPQvuoapfVXo37M09de9B\nyUyfQpRoDk8otNaLlFLlgfEYtzJ2AV211jdmuKkGpGZrv0kpFQlMzNgOAffLHBRCFB8HLhxl5eGV\nAOw8t5Nu9WRuOiFKukIZlKm1ngXMslLXyULZYmCxo+MSQjjG2z9l/fdt7t7HiZEIIQqLLF8uhLC7\nXcnfZ/7cOqC3EyMRQhQWeTJcCGF3Sx5dxOJ/FrP7/G4G96rn7HCEEIVAEgohhN3VCKjBiNYjnB2G\nEKIQyS0PIYQQQhSYJBRCCLvq0QNGjYI//3R2JEKIwiS3PIQQdpOcDNWqwddfw5YtklQIUZpID4UQ\nwm7c3eHjj+H0aVgsD34LUapIQiGEsIsxv41h1eFVaK0xmYzlbYQQpYckFEKIAotLjmPFvyvouqAr\nPb/p6exwhBBOIGMohBAF5uvuy1u1d/A/lw00CYlzdjhCCCeQhEIIYRfLlyvefbctrq7Q7G+oX9/Z\nEQkhCpPc8hBC2MU778DJk/DRR1BPJscUotSRHgohhN1UqwaDBjk7CiGEM0gPhRBCCCEKTBIKIcQt\n+ffyv5yPO09yMuzb5+xohBDOJgmFECLftNYMWDqA+h/U5/mvZ9KwcQohIbBokbMjE0I4iyQUQoh8\nW/jXQv488SexSbGsvfYxPyzRtG0Lfn7OjkwI4SwyKFMIkS9xyXG8vPrlzP1373mHbvXceUDmsxKi\nVJMeCiFEviSnJXN37bsBuK/+fXSr183JEQkhigLpoRBC5EtZr7LM6zWPoeFDKeshC3YIIQzSQyGE\nuCVNy7bmria1iYyEVaucHY0QwtkkoRBC3JK0NBgxAv7+G77/3tnRCCGcTW55CCFuib8//Oc/xpaY\n6OxohBDOJj0UQgibjl05Rkpais02np6FFIwQosiShEIIYVVqeipdvuxCxPwIzlw74+xwhBBFmCQU\nQgirXE2ufHH/FxyOOcyzy5/NLB8yBKZMgePHnRicEKJIkTEUQgib7qxxJ1FDokhNTwWMwZjXr8P4\n8TB/Pvzzj5MDFEIUCZJQCCHyVMk3a74JFxf46iuIi4Njx5wXkxCiaHHoLQ+lVKBS6iul1FWlVIxS\narZSyiePY9YppdKzbWlKqVmOjFMIkX++vtC4sbOjEEIUFY4eQ/E1EApEAPcCdwGf5HGMBj4FKgFB\nQGXgFQfGKITAWEH0VOwpZ4chhCimHJZQKKVCgK7AQK31dq31RuB5oK9SKiiPw+O11he11hcytjhH\nxSmEgJS0FIb8NISwT8LYc36P1XazZ8PSpcY4CiGEyM6RPRStgRit9c5sZWsweiBuz+PYx5RSF5VS\ne5VSk5RSXg6LUohS7lL8Jbos6MLsnbO5GH+Ru+ffTWxSrMW2S5ZAr17GrY7U1EIOVAhRpDlyUGYQ\ncCF7gdY6TSl1OaPOmq+A48AZoCnwNlAf6O2gOIUo1d7e8Dbrjq0DwMPFg3fveRd/D3+LbZcvh+3b\nYdcucJUh3UKIbPL9K0EpNRl41UYTjTFuwuopMtpYPljr2dl2/1ZKnQPWKKVqaa2PWjtuxIgRBAQE\nmJVFRkYSGRlpIxQhxJsd32T9ifUcjTnKj31/5I5qd9hs37KlsQkhipeFCxeycOFCs7KrV6/a7fxK\na6uf7ZYPUKocUC6PZkeAx4FpWuvMtkopFyAR6K21XnqT1/MG4oCuWuvVFurDgB07duwgLCzsJl+F\nECK7c3HnSElLoXpAdWeHIoQoRFFRUYSHhwOEa62jCnKufPdQaK0vAZfyaqeU2gSUUUq1yDaOIgKj\nh2JLPi7ZAqNH42x+YxVC3JwgX9vjpOfPh7ZtoXbtQgpICFHsOGxQptZ6P7AS+EwpdZtS6k7gfWCh\n1vocgFKqilJqn1KqZcZ+baXUWKVUmFIqWCnVE5gH/KG1/stRsQpR0qWmp/LRto9ITkvO97HXr8NL\nL0HdujBokAOCE0KUCI4eVvUo8AHG0x3pwPfAi9nq3TAGXHpn7CcDd2e08QFOAt8BEx0cpxAl2sFL\nB3nx1xc5FXuKiRH5++/k42PMiPn115DPO6RCiFLEoQmF1voK0M9G/XHAJdv+KaCDI2MSojRqWKEh\n49qPY/z68QxrNYwqflXydby3t/ROCCFskwe/hCglXm37KvfWvzffyYQQQtwMWb5ciBLiWtI1FuxZ\nYLXe1eRK86DmN30+rWHZMkhKskd0QoiSThIKIYq5K4lXGP/HeILfCebxJY/z54k/7XLef/+F3r2h\nRg347DO7nFIIUYJJQiFEMdflyy6MWzeOmMQYACb+zz5jmOvVg/37ISIC/C1PnCmEEJlkDIUQxVy/\npv3YdmYbLsqFx5o+xui2o+127tq1jac7hBAiL5JQCFEMxCTEEOgVaLHuyWZPsj96Py+1eYnagTLz\nlBDCOeSWhxBF3LSN02g4qyGp6ZaX9wzwDGDWvbPslkx89x2sX2+XUwkhShFJKIQo4jrW7Mi5uHOs\nObKmUK73+efQvj307ClLlAshbp4kFEI4UbpOZ9vpbUzbOA1rC/WFVQ6jT8M+pOv0Qolp+XL49lto\n1EiWKBdC3Dz5dSGEE8QlxzH056GsPLyS6PhoALrV7Uajio1ytVVKsajPokKLzWSChx82NiGEuFnS\nQyGEE/i4+fDH8T8ykwmAFf+ucGJEsk6HEKJgJKEQwgFiEmJ47IfH2HJqi8V6pRTd6nbD192XXiG9\n+KTHJ0Q2jizkKLP88IMxidXly04LQQhRzMktDyFuUVp6GkopTCp3Xu7v4c9PB36icYXG3F7tdovH\nT46YzAfdP8Ddxd3RoebJzQ3WroVmzWDbNggKcnZEQojiRhIKIW6S1pqPt3/MznM72X1+N3vP72Xj\nwI0W18dwMblwR7U72Hpmq9XzlfMu58hw8+W++2D3bpg/HypVcnY0QojiSBIKITJorbkYfxE/dz+8\n3Lxy1SulmLZpGkdijmSW7Tq3y+qCW/MfmE85r6KTNOSlenUYM8bZUQghiisZQyFEhhNXT1BpWiX+\nOP6H1TbZk4e6ZetafdQTIMg3CDcXN7vGaE+7d8P5886OQghRUkgPhSjRriRe4b/r/8uRmCMcvXKU\nozFHOTvqrMUeiOoB1fF09WR/9H7uqXuPxfO91vY1RrUeRZOKTfDz8HN0+A6jNQweDCdOGLc5unRx\ndkRCiOJOeihEsbbt9Dae+fkZqz0FbiY3pm+azpL9S9h1bhdXk65y/Opxi21NykT/Zv2p4lfF6vVa\nVmlJm+ptinUyAaAULFtmDMKcOFEeGRVCFJz0UIgiZ+/5vfx88GfOxZ3jbNxZagTUYFqXaRbbnr52\nmo93fMybHd+kok/FXPU+7j6U8yrHpYRLuJpcCQ4I5mriVavX/qjHR3Z7HUVdUBCsWAGxsUaCIYQQ\nBSEJhbArrTXXU67jolws3lYAGP/HeNLS03iz45sW66PORjH696wluJtWamr1etX9qwNw8upJiwkF\nwC+P/UJFn4pU86+Gq6n0/pOPjYUdO6Bjx6wykwnKlHFeTEKIkkNueYg8Jacls/LflZyLO2e1Tb8f\n+lFlehU8J3riN9mPL/d8abXtgUsHbA58DPI1nwTh7LWzVts2rNCQHUN20LBCQ6ttWlVtRc0yNUt1\nMgEwfTp07QpLlzo7EiFESVS6f8OWYDEJMUTHR1OvXD2L9WevneX1ta8TmxTL1aSrxCbFsvGpjSgL\nfd8JKQnc89U9LOq9iD6N+li+XmIMZ+OyPvgvxV+yGltF74rsPLvTan1Y5TC+7/M9lf0qU9m3cq4E\nIzsvNy/CKodZrRdZxo6Fv/+GZ56Bzp3B29vZEQkhShJJKIqQHWd2UN67PMFlgi3Wf/vXt6z4dwXX\nU65zLekaXep0YWTrkRbbfrLjE6ZtnEb0K9EW65PSkpizc45ZWVxynMXBhn4efpiUiZjEGKuxV/Gt\nQlW/qpTzLkc5r3JU9a9qte34juOZFDHJan0Fnwo81PAhq/Xi5qWmZq0Y6uYGCxfC8eOSTAgh7E8S\nihwSUhK4nHCZxNREElMTcXdxt/ot/+Clg3z393eMbD3S4ngBrTVtv2jL9eTrxKfEE58Sz+rHVxNa\nIdTi+R5c9CBPNH2CCZ0mWKzffmY783bPy9y3NmYAwM/dj9ikWKv1/h7+ucpik2ItJhQmZWJ8h/G0\nCGph9Xyf9fzMal2u2Ir5ExLFxbPPQnw8zJ2bVebmBnXrOi0kIUQJVmISive2vEfghUCS0pIYHDaY\nFpUtf/gNWjaIQM9ApnaZarH+i11fMOyXYZn77Wq0Y/2A9RbbHrx0kLFrxzIwbKDVmRV3ndtFfEp8\nZpmtD3lvN2+up1y3Wu/r7mu2H5ccZ7Vt44qN6de0H2npabiYXHLVl/Esw56he/D38CfAMwA/dz+L\n7W4Yc5dMoVjc3H479O8P994LfSzfqRJCCLspMQnFvF3z4ILxc4eaHawmFKdiT3El8YrV83i6eprt\nJ6QmWG3r5WokEdkTBktt0tLT8HbzxtvNG431B/4HtRhEnbJ1rNYPbTmUhxs9jK+7Lz7uPvi5W/+m\n375me9rXbG+13qRMNKnUxGq9KF60NsZHNG6cVfbEE/Dnn86LSQhRupSYhCK7pNQkq3UBngE2j60d\nWJsHQh7Ay80LTxdPagfWttq2WVAzlvZdavPWw4WXL1hcjdKSUW1G2ayv5FuJSr6ycpPI7ZNPYPhw\nOPMOKGIAAA4wSURBVHoUKlc2ypSCz27+TpQQQhSIsrUWQXGglAoDdny87GOaNm+Kh6sHNcvUpKxX\nWWeHJoRDpKTAwYPQqFFW2dWrULMmDBgAM2Y4LTQhRDETFRVFeHg4QLjWOqog53LYPBRKqdFKqQ1K\nqetKqcv5OG68UuqMUipeKbVaKXVTQ8huq3obrau3JqxymCQTokR76y24805IytYRFxBgzDMREeG8\nuIQQpZsjJ7ZyAxYBNz2XsVLqVeA54GmgFXAdWKmUcndIhEIUcYMHwxtvmJc98IDRI7F6tXn5U08Z\nAzCFEMIZHJZQaK3f1Fq/C+zNx2EvAhO01j9prf8CngCqAL0cEaMQjpacDGlp5mXr18OqVeZl+/ZB\nhQrGn9nFx8Ovv5qXNWoEI0ZARetDd4QQotAVmam3lVK1gCDgtxtlWutYYAvQOq/jt2413z9+3LiX\nfDXHOlBLlhirLGZ3+bLR9vRp8/LVq+Grr8zLUlKMtvv3m5dv3mwMjMtp1izYts287O+/jXOkp5uX\nz58Pv/9ecl/H9Om5X8cPP+SeCvryZZg2DU6dMi9ftQq+zDGjd3KycQsg5wfxn3/Cu+/mfh1TpsDG\njeZl27fDf/6Te8XNiRPh++/Ny3buhN69jRize+YZeO213Of18IC9OVLqWbOMmLMLDIToaDhwwLy8\nTRvj/UxNNS+fMQNatcr9+oQQwlmKTEKBkUxo4HyO8vMZdTatW2e+f+gQjBoFMTkmd5w923yiH4CL\nF422R4+al3//PbzzjnlZcrLRdmeOmaPXrMndNQ3GdMc5Y9u2zThHzg/iadPgxx9L7ut46aXcr2PO\nHJg3z7zs4kV4+WU4dsy8fPFieP9987KUFCMZ2LXLvHztWiN5yGnKFNiwwbxs7/+3d/cxltV3Hcff\nH5YFwjY8FXmUUNqC1ocSltjSRhBK0oimYGKibknZLolgxMasNkWRCKXRNYBdRIupMaGPmWaFoKjU\nBQTdLSkgM7bSle2W7vIkXVxKshSkhe5+/eOcKTN3Zncezty9c5f3K/mFub97zrnfm8PZ+cw5v3N+\njza/4HsDxZe+BBs3Tq3t9tvhpZ5HgDzxRPMdJ3pT+9iQ3mWPPHJqIDnmGDjllOaMxESXXQZPPfX6\n0y4labGa0z9TSdYAV+5lkQLeUVVbOlXV87Htdvdq48bVXHjh67eE7tgBsKJteqPZ03TcydTgML7s\nrl3N7JvjDjhg6uWKg9rRPK++Orl/2bKp2z3ySDjnHDhk8qNNuPhieN/7JvcdcABs3Tq13qVLp/8e\nkjRXIyMjjIyMTOrb2XvauIuqmnUD3gycNkM7sGedlcALs9j2KcBu4J09/f8GrN3LesuB+uIXR2ui\n7dur1q2reumlSd21YUPVAw9M7nvxxWbZHTsm9z/ySNU990zue+21Ztknn5zcv2lT1Z131hR33FG1\nefPkvm3bmm3s2jW5f/36qrGxyX37+/fYuHH673HbbVO/x9hY1X33Tf0ed9xR9fTTk/u3bKm6++6p\n3+Pee6u2bp3c953vVN1//9TvsWlT1VNPTe575ZWqZ55pPleSht3o6GjR/NG+vOaQB6ZrfX8ORZKV\nbSCY8V7OJM8CN1TV2vb1YTSXPC6pqr/bwzrLgdHR0VGWL3fWSUmSZmtYnkNxUpLTgZOBJUlOb9uy\nCctsTnLRhNVuAq5O8oEkPwt8DngG6Bm2J0mSFpN+DvW6jua2z3Hjyec8YHy2rVOBHw18qKrrkxwK\nfBo4AtgIXFBVPVesJUnSYtK3QFFVq4BVMywzZXrLqroWuLY/VUmSpH5YTLeNSpKkIWWgkCRJnRko\nJElSZwYKSZLUmYFCkiR1ZqCQJEmdGSgkSVJnBgpJktSZgUKSJHVmoJAkSZ0ZKCRJUmcGCkmS1JmB\nQpIkdWagkCRJnRkoJElSZwYKSZLUmYFCkiR1ZqCQJEmdGSgkSVJnBgpJktSZgUKSJHVmoJAkSZ0Z\nKCRJUmcGCkmS1JmBQpIkdWagkCRJnRkoJElSZ30LFEmuSvJAkpeTvDDLdW5Nsrun3dWvGiVJ0sI4\nsI/bXgqsA74KXDqH9b4MfBhI+/oHC1uWJElaaH0LFFX1cYAkK+e46g+qakcfSpIkSX2yGMdQnJvk\nuSSbk9yS5KhBFyRJkvaun5c85uPLwO3ANuBtwBrgriTvqaoaaGWSJGmP5hQokqwBrtzLIgW8o6q2\nzKeYqlo34eWmJI8C3wbOBe6fzzYlSVL/zfUMxY3ArTMss3WetUxRVduSPA+8nRkCxerVqzn88MMn\n9a1YsYIVK1YsVDmSJA2tkZERRkZGJvXt3Llzwbaffl9JaAdlrq2qOY+FSPLjwJPARVX1T3tYZjkw\nOjo6yvLly7sVK0nSG8jY2BhnnnkmwJlVNdZlW/18DsVJSU4HTgaWJDm9bcsmLLM5yUXtz8uSXJ/k\n3UlOTnI+8PfAFmB9v+qUJEnd9XNQ5nXAJRNejyef84AN7c+nAuPXKXYB72zXOQJ4liZI/HFVvdbH\nOiVJUkf9fA7FKmDVDMssmfDz94Ff7Fc9kiSpfxbjcygkSdKQMVBIkqTODBSSJKkzA4UkSerMQCFJ\nkjozUEiSpM4MFJIkqTMDhSRJ6sxAIUmSOjNQSJKkzgwUkiSpMwOFJEnqzEAhSZI6M1BIkqTODBSS\nJKkzA4UkSerMQCFJkjozUEiSpM4MFJIkqTMDhSRJ6sxAIUmSOjNQSJKkzgwUkiSpMwOFJEnqzEAh\nSZI6M1BIkqTODBSSJKkzA4UWnZGRkUGXoAXmPt2/uD81nb4FiiQnJ/nbJFuT/F+SbyW5NsnSGdY7\nOMmnkjyf5HtJbktyTL/q1OLjP1b7H/fp/sX9qen08wzFTwIBfhP4KWA18FvAn8yw3k3ALwO/CpwD\nnADc3r8yJUlSVwf2a8NVtR5YP6HriSQ30oSKj023TpLDgEuB36iqf2/7VgGPJXlXVT3cr3olSdL8\n7esxFEcAL+zl/TNpQs6/jndU1TeBp4D39Lc0SZI0X307Q9EryduB3wF+by+LHQe8WlUv9vQ/1743\nnUMAHnvssc41anHYuXMnY2Njgy5DC8h9un9xf+4/JvzuPKTzxqpqTg1YA+zeS9sFnNazzonAt4BP\nz7DtFcAr0/Q/DPzpHtb5IFA2m81ms9nm3T441zzQ2+ZzhuJG4NYZltk6/kOSE4D7gK9U1eUzrLcd\nOCjJYT1nKY6hOUsxnfXAxcATwPdn2L4kSXrdIcBbmDzmcV7S/pXfF0lOpAkT/wF8qGb4sHZQ5g6a\nQZl3tH2nAZuBsxyUKUnS4tS3QJHkeGADzZmDlTSXQgCoqufaZU6gGYD5oap6pO27BbgAWAV8D7gZ\n2F1VZ/elUEmS1Fk/B2W+H3hr255u+0JzrWZJ+3opcBpw6IT1VtOEj9uAg4F/Aa7oY52SJKmjvl7y\nkCRJbwzO5SFJkjozUEiSpM6GOlAkuSrJA0leTjLtEziTnJTkn9tltie5PslQf+83kiRPJNk9oe1K\nMu2j27X4JLkiybYkryR5MMnPDbomzU+Sa3qOxd1J/nvQdWl2kpyd5M4k/9PuuwunWea6JM+2E3re\n0z6QctaG/RfrUmAd8NfTvdkGh7toBp+eRXO3yYeB6/ZRfequgKuBY2melno88JcDrUizkuTXgT8H\nrgHOAL4OrE9y9EALUxff4PVj8Tjg5wdbjuZgGfA1mpscpgyeTHIlzdOsLwfeBbxMc7weNNsP2C8G\nZSZZCaytqqN6+i8A7gSOr6rn277LgT8DfqyqfrjPi9WcJNlGs29vHnQtmpskDwIPVdXvtq9Dc8fX\nzVV1/UCL05wluQa4qKqWD7oWdZNkN/ArVXXnhL5ngRuqam37+jCaB0qurKp1s9nusJ+hmMlZwKPj\nYaK1Hjgc+OnBlKR5+IMkzycZS/LRJEtmXkWDlGQpzWR/Eyf6K+BenOhvmJ3anjL/dpIvJDlp0AWp\nuySn0Jxxmni8vgg8xByO1302OdiAHMfUR3Y/N+G9r+/bcjQPfwGM0cxS+16as0vHAR8dZFGa0dE0\nz5uZ7vj7iX1fjhbAgzSXjL9Jc+nxWmBDkp+pqpcHWJe6O47mMsh0x+ueJuacYtGdoUiyZpqBP72D\n8k5bgI8a/ms9Q2ou+7iqbqqqDVX1jar6G+D3gY+0fwFr+Iw/3E5DpqrWV9Xt7bF4D/BLwJHArw24\nNPXPnI7XxXiGYk6Tj81gO9A7qvzY9r97mmxM/ddlHz9E8//tW2hmsNXi9DzNE2+P7enf20R/GiJV\ntTPJFmBOdwJoUdpOEx6OZfLxeQzwn7PdyKILFFX1XeC7C7S5rwJXJTl6wjiK9wM7AW93GpCO+/gM\nYDfwvwtXkRZaVb2WZBQ4n2Zg9PigzPNp5ufRkEvyJuBtwOcGXYu6qaptSbbTHJ//BT8alPlu4FOz\n3c6iCxRz0Q4IOgo4GViS5PT2rcfba3p30wSHz7e3xBwPfAL4q6p6bRA1a/aSnEXzP/T9NBPFvRf4\nJPD5qto5yNo0K58EPtsGi4dp5uk5FPjMIIvS/CS5AfhH4EngRODjwA+BkUHWpdlJsozmbFLarre2\nvzNfqKqngZuAq5M8TjOp5yeAZ4B/mPVnDPNto0luBS6Z5q3zqmpDu8xJNM+pOJfmvtrPAH9YVbv3\nUZmapyRnALfQDOI7GNhG89fQWgPhcEjy28DHaE6lfg34yPjMwhouSUaAs4E3AzuArwB/VFXbBlqY\nZiXJL9D8cdb7S/+zVXVpu8y1wGXAEcBG4IqqenzWnzHMgUKSJC0Oi+4uD0mSNHwMFJIkqTMDhSRJ\n6sxAIUmSOjNQSJKkzgwUkiSpMwOFJEnqzEAhSZI6M1BIkqTODBSSJKkzA4UkSers/wEi0TISA/0F\nRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13082379b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_vals, y_softplus, 'r--', label='Softplus', linewidth=2)\n",
    "plt.plot(x_vals, y_relu, 'b:', label='ReLU', linewidth=2)\n",
    "plt.plot(x_vals, y_relu6, 'g-.', label='ReLU6', linewidth=2)\n",
    "plt.plot(x_vals, y_elu, 'k-', label='ExpLU', linewidth=0.5)\n",
    "plt.ylim([-1.5,7])\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(x_vals, y_sigmoid, 'r--', label='Sigmoid', linewidth=2)\n",
    "plt.plot(x_vals, y_tanh, 'b:', label='Tanh', linewidth=2)\n",
    "plt.plot(x_vals, y_softsign, 'g-.', label='Softsign', linewidth=2)\n",
    "plt.ylim([-2,2])\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Acivation_Functions1](https://github.com/nfmcclure/tensorflow_cookbook/raw/jupyter_notebooks/01_Introduction/images/06_activation_funs1.png)\n",
    "\n",
    "![Acivation_Functions2](https://github.com/nfmcclure/tensorflow_cookbook/raw/jupyter_notebooks/01_Introduction/images/06_activation_funs2.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
